1. Field of the Invention
The present invention relates to a Multiple-Input Multiple-Output (MIMO) wireless communication system. More particularly, the present invention relates to an apparatus and a method for generating per stream Effective Signal to Noise Ratios (ESNRs) for a Maximum Likelihood (ML) detection in the MIMO wireless communication system.
2. Description of the Related Art
Over time, the demand for high-speed and high-quality data transmission in a wireless communication system has grown. To meet these increasing demands, a Multiple-Input Multiple-Output (MIMO) wireless communication system using a plurality of transmit antennas and a plurality of receive antennas is attracting attention. In a MIMO system, communications are performed using a plurality of streams via the multiple antennas. Compared to a single antenna, the channel capacity in a MIMO system is greatly enhanced. For example, in a MIMO system in which the transmitting end uses M-ary transmit antennas, the receiving end uses M-ary receive antennas, channels between the antennas are independent of each other, and the bandwidth and the entire transmit power are fixed, the average channel capacity is increased M times as compared to a single antenna.
There are various detection schemes for detecting the intended signals from among the signals received at the receive antennas in a MIMO system. Among the various detection schemes, a Maximum Likelihood (ML) detection scheme exhibits the highest performance. As compared to a linear scheme, such as Minimum Mean Square Error (MMSE) detection scheme that provides a diversity gain less than the number of the receive antennas, the ML detection scheme guarantees a diversity gain equal to the number of the receive antennas. However, the ML detection scheme has very high operational complexity which complicates its applications in spite of its optimum performance.
Recently, research is being conducted to provide a detection scheme having lower operational complexity while having performance close to the ML detection scheme. As a result, various approaches such as QR Decomposition-Modified Maximum Likelihood Detector (QRM-MLD), Recursive Modified Maximum Likelihood (RMML), and Sorted-RMML (S-RMML) are suggested. However, those approaches pertain to MIMO technology using an Open Loop (OL). That is, the above-mentioned approaches do not consider how to utilize feedback information from the receiving end or how to generate the feedback information. To apply the ML detection to the MIMO technology using a Closed Loop (CL), a method for generating the feedback information suitable for the ML detection is needed.